Intelligent Diagnosis of Rotating Machinery Based on Optimized Adaptive Learning Dictionary and 1DCNN

In the intelligent fault diagnosis of rotating machinery, it is difficult to extract early weak fault impact features of rotating machinery under the interference of strong background noise, which makes the accuracy of fault identification low. In order to effectively identify the early faults of rotating machinery, an intelligent fault diagnosis method of rotating machinery based on an optimized adaptive learning dictionary and one-dimensional convolution neural network (1DCNN) is proposed in this paper. First of all, based on the original signal, a redundant dictionary with impact components is constructed by K-singular value decomposition (K-SVD), and the sparse coefficients are solved by an optimized orthogonal matching pursuit (OMP) algorithm. The sparse representation of fault impact features is realized, and the reconstructed signal with a concise fault impact feature structure is obtained. Secondly, the reconstructed signal is normalized, and the experimental dataset is divided into samples. Finally, the training set is input into the 1DCNN model for model training, and the test set is input into the trained model for classification and detection to complete the intelligent fault classification diagnosis of rotating machinery. This method is applied to the fault diagnosis of bearing data of Case Western Reserve University and worm gear reducer data of Shanghai University of Technology. Compared with other methods and models, the results show that the diagnosis method proposed in this paper can achieve higher diagnosis accuracy and better generalization ability than other diagnosis models under different datasets.

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An intelligent fault diagnosis method for rotating machinery based on data fusion and deep residual neural network

Applied Intelligence ◽

10.1007/s10489-021-02555-4 ◽

2021 ◽

Author(s):

Binsen Peng ◽

Hong Xia ◽

Xinzhi Lv ◽

M. Annor-Nyarko ◽

Shaomin Zhu ◽

...

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Data Fusion ◽

Rotating Machinery ◽

Intelligent Fault Diagnosis ◽

Diagnosis Method

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A Generative Adversarial Network-Based Intelligent Fault Diagnosis Method for Rotating Machinery Under Small Sample Size Conditions

IEEE Access ◽

10.1109/access.2019.2947194 ◽

2019 ◽

Vol 7 ◽

pp. 149736-149749 ◽

Cited By ~ 7

Author(s):

Yu Ding ◽

Liang Ma ◽

Jian Ma ◽

Chao Wang ◽

Chen Lu

Keyword(s):

Fault Diagnosis ◽

Sample Size ◽

Small Sample Size ◽

Rotating Machinery ◽

Small Sample ◽

Intelligent Fault Diagnosis ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Diagnosis Method

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A novel unsupervised deep learning network for intelligent fault diagnosis of rotating machinery

Structural Health Monitoring ◽

10.1177/1475921719897317 ◽

2020 ◽

Vol 19 (6) ◽

pp. 1745-1763 ◽

Cited By ~ 8

Author(s):

Xiaoli Zhao ◽

Minping Jia

Keyword(s):

Deep Learning ◽

Fault Diagnosis ◽

Rotating Machinery ◽

Affinity Propagation ◽

Intelligent Fault Diagnosis ◽

Learning Network ◽

Feature Extractor ◽

Unsupervised Deep Learning ◽

Diagnosis Method ◽

Deep Learning Network

Generally, the health conditions of rotating machinery are complicated and changeable. Meanwhile, its fault labeled information is mostly unknown. Therefore, it is man-sized to automatically capture the useful fault labeled information from the monitoring raw vibration signals. That is to say, the intelligent unsupervised learning approach has a significant influence on fault diagnosis of rotating machinery. In this study, a span-new unsupervised deep learning network can be constructed based on the proposed feature extractor (L12 sparse filtering (L12SF)) and the designed clustering extractor (Weighted Euclidean Affinity Propagation) for resolving the issue that the acquisition of fault sample labeled information is burdensome, yet costly. Naturally, the novel intelligent fault diagnosis method of rotating machinery based on unsupervised deep learning network is first presented in this study. Thereinto, the proposed unsupervised deep learning network consists of two layers of unsupervised feature extractor (L12SF) and one layer of unsupervised clustering (Weighted Euclidean Affinity Propagation). L12SF can improve the regularization performance of sparse filtering, and Weighted Euclidean Affinity Propagation can get rid of the traditional Euclidean distance in affinity propagation that cannot highlight the contribution of different features in fault clustering. To make a long story short, the frequency spectrum signals are first entered into the constructed unsupervised deep learning network for fault feature representation; afterward, the unsupervised feature learning and unsupervised fault classification of rotating machinery can be implemented. The superiority of the proposed algorithms and method is validated by two cases of rolling bearing fault dataset. Ultimately, the proposed unsupervised fault diagnosis method can provide a theoretical basis for the development of intelligent unsupervised fault diagnosis technology for rotating machinery.

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A novel intelligent fault diagnosis method based on variant sparse filtering and back-propagation

Noise & Vibration Worldwide ◽

10.1177/09574565211055786 ◽

2021 ◽

pp. 095745652110557

Author(s):

Lifeng Chan ◽

Chun Cheng

Keyword(s):

Fault Diagnosis ◽

Back Propagation ◽

Feature Learning ◽

Rotating Machinery ◽

Learning Ability ◽

Back Propagation Algorithm ◽

Intelligent Fault Diagnosis ◽

Propagation Algorithm ◽

Sparse Filtering ◽

Diagnosis Method

Detecting the mechanical faults of rotating machinery in time plays a key role in avoiding accidents. With the coming of the big data era, intelligent fault diagnosis methods based on machine learning models have become promising tools. To improve the feature learning ability, an unsupervised sparse feature learning method called variant sparse filtering is developed. Then, a fault diagnosis method combining variant sparse filtering with a back-propagation algorithm is presented. The involvement of the back-propagation algorithm can further optimize the weight matrix of variant sparse filtering using label data. At last, the developed diagnosis method is validated by rolling bearing and planetary gearbox experiments. The experiment results indicate that the developed method can achieve high accuracy and good stability in rotating machinery fault diagnosis.

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Intelligent Fault Diagnosis Method for Coupling Rotating Machinery Based on Deep Convolutional Neural Network

2019 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS) ◽

10.1109/safeprocess45799.2019.9213260 ◽

2019 ◽

Author(s):

Dawei Mu ◽

Li Sheng

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Convolutional Neural Network ◽

Rotating Machinery ◽

Deep Convolutional Neural Network ◽

Intelligent Fault Diagnosis ◽

Diagnosis Method

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A New Intelligent Fault Diagnosis Method of Rotating Machinery under Varying-Speed Conditions Using Infrared Thermography

Complexity ◽

10.1155/2019/2619252 ◽

2019 ◽

Vol 2019 ◽

pp. 1-12 ◽

Cited By ~ 3

Author(s):

Yongbo Li ◽

Xianzhi Wang ◽

Shubin Si ◽

Xiaoqiang Du

Keyword(s):

Support Vector Machine ◽

Fault Diagnosis ◽

Infrared Thermography ◽

Rotating Machinery ◽

Support Vector ◽

Intelligent Fault Diagnosis ◽

Fault Features ◽

Diagnosis Method ◽

Systematic Framework ◽

Machinery Fault Diagnosis

A novel systematic framework, infrared thermography- (IRT-) based method, for rotating machinery fault diagnosis under nonstationary running conditions is presented in this paper. In this framework, IRT technique is first applied to obtain the thermograph. Then, the fault features are extracted using bag-of-visual-word (BoVW) from the IRT images. In the end, support vector machine (SVM) is utilized to automatically identify the fault patterns of rotating machinery. The effectiveness of proposed method is evaluated using lab experimental signal of rotating machinery. The diagnosis results show that the IRT-based method has certain advantages in classification rotating machinery faults under nonstationary running conditions compared with the traditional vibration-based method.

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An Intelligent Fault Diagnosis Method of Multi-Scale Deep Feature Fusion Based on Information Entropy

Chinese Journal of Mechanical Engineering ◽

10.1186/s10033-021-00580-5 ◽

2021 ◽

Vol 34 (1) ◽

Author(s):

Zhiwu Shang ◽

Wanxiang Li ◽

Maosheng Gao ◽

Xia Liu ◽

Yan Yu

Keyword(s):

Feature Extraction ◽

Fault Diagnosis ◽

Information Entropy ◽

Feature Fusion ◽

Fault Classification ◽

Intelligent Fault Diagnosis ◽

Test Bed ◽

Multi Scale ◽

Deep Feature ◽

Diagnosis Method

AbstractFor a single-structure deep learning fault diagnosis model, its disadvantages are an insufficient feature extraction and weak fault classification capability. This paper proposes a multi-scale deep feature fusion intelligent fault diagnosis method based on information entropy. First, a normal autoencoder, denoising autoencoder, sparse autoencoder, and contractive autoencoder are used in parallel to construct a multi-scale deep neural network feature extraction structure. A deep feature fusion strategy based on information entropy is proposed to obtain low-dimensional features and ensure the robustness of the model and the quality of deep features. Finally, the advantage of the deep belief network probability model is used as the fault classifier to identify the faults. The effectiveness of the proposed method was verified by a gearbox test-bed. Experimental results show that, compared with traditional and existing intelligent fault diagnosis methods, the proposed method can obtain representative information and features from the raw data with higher classification accuracy.

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